# OpenMix: Exploring Outlier Samples for Misclassification Detection

arXiv: https://arxiv.org/abs/2303.17093 · HTML: https://mlsanity.com/paper/2303.17093/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 70 references that are in the map.
- First posted on arXiv 30 Mar 2023.

## Builds on (most cited first, 30 of 52)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [Scaling Out-of-Distribution Detection for Real-World Settings](https://mlsanity.com/paper/1911.11132/) (2019)
- [Calibrating Deep Neural Networks using Focal Loss](https://mlsanity.com/paper/2002.09437/) (2020)
- [On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks](https://mlsanity.com/paper/1905.11001/) (2019)
- [Exploring the Limits of Out-of-Distribution Detection](https://mlsanity.com/paper/2106.03004/) (2021)
- [Mitigating Neural Network Overconfidence with Logit Normalization](https://mlsanity.com/paper/2205.09310/) (2022)
- [To Trust Or Not To Trust A Classifier](https://mlsanity.com/paper/1805.11783/) (2018)
- [Addressing Failure Prediction by Learning Model Confidence](https://mlsanity.com/paper/1910.04851/) (2019)
- [Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art](https://mlsanity.com/paper/1704.05519/) (2017)
- [Confidence-Aware Learning for Deep Neural Networks](https://mlsanity.com/paper/2007.01458/) (2020)
- [Revisiting Training Strategies and Generalization Performance in Deep Metric Learning](https://mlsanity.com/paper/2002.08473/) (2020)

## Built on by (most cited first, 30 of 38)

- [Overcoming Common Flaws in the Evaluation of Selective Classification Systems](https://mlsanity.com/paper/2407.01032/) (2024)
- [Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning](https://mlsanity.com/paper/2312.10686/) (2023)
- [Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection](https://mlsanity.com/paper/2409.04796/) (2024)
- [A Data-Driven Measure of Relative Uncertainty for Misclassification Detection](https://mlsanity.com/paper/2306.01710/) (2023)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Gradient-Regularized Out-of-Distribution Detection](https://mlsanity.com/paper/2404.12368/) (2024)
- [Revisiting Confidence Estimation: Towards Reliable Failure Prediction](https://mlsanity.com/paper/2403.02886/) (2024)
- [Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers](https://mlsanity.com/paper/2406.18451/) (2024)
- [Unified Classification and Rejection: A One-versus-All Framework](https://mlsanity.com/paper/2311.13355/) (2023)
- [Energy-based Hopfield Boosting for Out-of-Distribution Detection](https://mlsanity.com/paper/2405.08766/) (2024)
- [SURE: SUrvey REcipes for building reliable and robust deep networks](https://mlsanity.com/paper/2403.00543/) (2024)
- [Interpretable Failure Detection with Human-Level Concepts](https://mlsanity.com/paper/2502.05275/) (2025)
- [Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward](https://mlsanity.com/paper/2404.08517/) (2024)
- [Typicalness-Aware Learning for Failure Detection](https://mlsanity.com/paper/2411.01981/) (2024)
- [Out-Of-Distribution Detection with Diversification (Provably)](https://mlsanity.com/paper/2411.14049/) (2024)
- [Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization](https://mlsanity.com/paper/2506.02334/) (2025)
- [Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes](https://mlsanity.com/paper/2402.01476/) (2024)
- [Zero-Shot Out-of-Distribution Detection with Outlier Label Exposure](https://mlsanity.com/paper/2406.01170/) (2024)
- [Margin-bounded Confidence Scores for Out-of-Distribution Detection](https://mlsanity.com/paper/2410.07185/) (2024)
- [Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation](https://mlsanity.com/paper/2410.20807/) (2024)
- [All Beings Are Equal in Open Set Recognition](https://mlsanity.com/paper/2401.17654/) (2024)
- [To Trust Or Not To Trust Your Vision-Language Model's Prediction](https://mlsanity.com/paper/2505.23745/) (2025)
- [Evaluation and Improvement of Fault Detection for Large Language Models](https://mlsanity.com/paper/2404.14419/) (2024)
- [Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information](https://mlsanity.com/paper/2503.09068/) (2025)
- [Towards Efficient and General-Purpose Few-Shot Misclassification Detection for Vision-Language Models](https://mlsanity.com/paper/2503.20492/) (2025)
- [Enhancing Multi-view Open-set Learning via Ambiguity Uncertainty Calibration and View-wise Debiasing](https://mlsanity.com/paper/2508.01227/) (2025)
- [Adaptive Confidence Regularization for Multimodal Failure Detection](https://mlsanity.com/paper/2603.02200/) (2026)
- [Be Bayesian by Attachments to Catch More Uncertainty](https://mlsanity.com/paper/2310.13027/) (2023)
- [RICASSO: Reinforced Imbalance Learning with Class-Aware Self-Supervised Outliers Exposure](https://mlsanity.com/paper/2410.10548/) (2024)
- [Mining In-distribution Attributes in Outliers for Out-of-distribution Detection](https://mlsanity.com/paper/2412.11466/) (2024)

## Abstract (arXiv)

Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental requirement in high-stakes applications. Unfortunately, modern deep neural networks are often overconfident for their erroneous predictions. In this work, we exploit the easily available outlier samples, i.e., unlabeled samples coming from non-target classes, for helping detect misclassification errors. Particularly, we find that the well-known Outlier Exposure, which is powerful in detecting out-of-distribution (OOD) samples from unknown classes, does not provide any gain in identifying misclassification errors. Based on these observations, we propose a novel method called OpenMix, which incorporates open-world knowledge by learning to reject uncertain pseudo-samples generated via outlier transformation. OpenMix significantly improves confidence reliability under various scenarios, establishing a strong and unified framework for detecting both misclassified samples from known classes and OOD samples from unknown classes. The code is publicly available at https://github.com/Impression2805/OpenMix.